Aditya Desai, Divya Tankasala, Gabriel P Ng, Pankti Thakkar, Orlando S Hoilett, Kieren J Mather, Jacqueline C Linnes
{"title":"Selective Collection of Exhaled Breath Condensate for Noninvasive Screening of Breath Glucose.","authors":"Aditya Desai, Divya Tankasala, Gabriel P Ng, Pankti Thakkar, Orlando S Hoilett, Kieren J Mather, Jacqueline C Linnes","doi":"10.1177/19322968231179728","DOIUrl":"10.1177/19322968231179728","url":null,"abstract":"<p><strong>Background: </strong>Although exhaled breath condensate (EBC) is a promising noninvasive sample for detecting respiratory analytes such as glucose, current EBC collection methods yield inconsistent results.</p><p><strong>Methods: </strong>We developed a custom EBC collection device with a temperature-based algorithm to selectively condense alveolar air for reproducible EBC glucose detection. We characterized the condensate volumes and the corresponding glucose concentrations. We performed a pilot study demonstrating its use during oral glucose tolerance tests.</p><p><strong>Results: </strong>The novel device selectively captured alveolar air resulting in slightly higher and less variable glucose concentrations than the overall EBC. Participants with type 2 diabetes demonstrated significantly higher blood plasma-EBC glucose ratios than normoglycemic participants.</p><p><strong>Conclusions: </strong>Temperature-based selective EBC collection allows EBC glucose measurement and is a promising sampling method to distinguish patients with and without diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"161-164"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10124228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michael S Hughes, Grazia Aleppo, Lia Bally, Annanda Fernandes Moura B Batista, Sue A Brown, Eileen R Faulds, Linda A Gonder-Frederick, Diana Isaacs, Anna R Kahkoska, Jacob Ortega, William H Polonsky, Meaghan M Stumpf
{"title":"Diabetes Technology Use in Special Populations: A Narrative Review of Psychosocial Factors.","authors":"Michael S Hughes, Grazia Aleppo, Lia Bally, Annanda Fernandes Moura B Batista, Sue A Brown, Eileen R Faulds, Linda A Gonder-Frederick, Diana Isaacs, Anna R Kahkoska, Jacob Ortega, William H Polonsky, Meaghan M Stumpf","doi":"10.1177/19322968241296853","DOIUrl":"10.1177/19322968241296853","url":null,"abstract":"<p><p>As diabetes technologies continue to advance, their use is expanding beyond type 1 diabetes to include populations with type 2 diabetes, older adults, pregnant individuals, those with psychiatric conditions, and hospitalized patients. This review examines the psychosocial outcomes of these technologies across these diverse groups, with a focus on treatment satisfaction, quality of life, and self-management behaviors. Despite demonstrated benefits in glycemic outcomes, the adoption and sustained use of these technologies face unique challenges in each population. By highlighting existing research and identifying gaps, this review seeks to emphasize the need for targeted studies and tailored support strategies to understand and optimize psychosocial outcomes and well-being.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"34-46"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11577555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142675981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Molly L Tanenbaum, Persis V Commissariat, Emma G Wilmot, Karin Lange
{"title":"Navigating the Unique Challenges of Automated Insulin Delivery Systems to Facilitate Effective Uptake, Onboarding, and Continued Use.","authors":"Molly L Tanenbaum, Persis V Commissariat, Emma G Wilmot, Karin Lange","doi":"10.1177/19322968241275963","DOIUrl":"10.1177/19322968241275963","url":null,"abstract":"<p><p>Advances in diabetes technologies have enabled automated insulin delivery (AID) systems, which have demonstrated benefits to glycemia, psychosocial outcomes, and quality of life for people with type 1 diabetes (T1D). Despite the many demonstrated benefits, AID systems come with their own unique challenges: continued user attention and effort, barriers to equitable access, personal costs vs benefits, and integration of the system into daily life. The purpose of this narrative review is to identify challenges and opportunities for supporting uptake and onboarding of AID systems to ultimately support sustained AID use. Setting realistic expectations, providing comprehensive training, developing willingness to adopt new treatments and workflows, upskilling of diabetes team members, and increasing flexibility of care to tailor care to individual needs, preferences, lifestyle, and personal goals will be most effective in facilitating effective, widespread, person-centered implementation of AID systems.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"47-53"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571946/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142107939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kimberly P Garza, Kelsey R Howard, Marissa Feldman, Jill Weissberg-Benchell
{"title":"Adult's Lived Experience Using the Insulin-Only Bionic Pancreas.","authors":"Kimberly P Garza, Kelsey R Howard, Marissa Feldman, Jill Weissberg-Benchell","doi":"10.1177/19322968241274364","DOIUrl":"10.1177/19322968241274364","url":null,"abstract":"<p><strong>Background: </strong>The purpose of this study was to assess adults' perspectives after using the insulin-only Bionic Pancreas (BP) during a 13-week pivotal trial. Automated insulin delivery (AID) systems show promise in improving glycemic outcomes and reducing disease burden for those with type 1 diabetes mellitus (T1D). Understanding the lived experience of those using the BP can help to inform education and uptake of AID devices.</p><p><strong>Methods: </strong>Adults ages 19 to 75 (n = 40) participated in age-specific focus groups (19-25, 26-40, 41-64, and 65+) exploring their experiences, thoughts, and feelings about using the BP. Three authors analyzed the focus group data using a hybrid thematic approach.</p><p><strong>Results: </strong>Qualitative analysis of focus groups revealed 14 sub-themes falling into four major themes (diabetes burden, managing glucose levels, daily routine, and user experience). Although participants' overall experience was positive, some reported struggles related to managing out-of-range glucose levels and challenges with the system responding to unique meal schedules and exercise regimens.</p><p><strong>Conclusion: </strong>This study captures patient perspectives regarding their experiences with a new AID system. Patient voice can inform device development and educational approaches for people with T1D. Identifying which patients may benefit the most from wearing this system may facilitate patient/clinician discussions regarding insulin delivery systems that best meet their individualized needs and expectations that may support device uptake and continued use.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"11-17"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142107938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimizing Duration of Usage of Insulin Infusion Sets: Impact of Mechanical Stress on Infusion Sites and Identifying Individuals With IIS Issues.","authors":"John Walsh, Lutz Heinemann","doi":"10.1177/19322968241233607","DOIUrl":"10.1177/19322968241233607","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"3-4"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139912738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alessandra T Ayers, Cindy N Ho, David Kerr, Simon Lebech Cichosz, Nestoras Mathioudakis, Michelle Wang, Bijan Najafi, Sun-Joon Moon, Ambarish Pandey, David C Klonoff
{"title":"Artificial Intelligence to Diagnose Complications of Diabetes.","authors":"Alessandra T Ayers, Cindy N Ho, David Kerr, Simon Lebech Cichosz, Nestoras Mathioudakis, Michelle Wang, Bijan Najafi, Sun-Joon Moon, Ambarish Pandey, David C Klonoff","doi":"10.1177/19322968241287773","DOIUrl":"10.1177/19322968241287773","url":null,"abstract":"<p><p>Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artificial intelligence is technology that enables computers and machines to simulate human intelligence and solve complicated problems. In this article, we address current and likely future applications for AI to be applied to diabetes and its complications, including pharmacoadherence to therapy, diagnosis of hypoglycemia, diabetic eye disease, diabetic kidney diseases, diabetic neuropathy, diabetic foot ulcers, and heart failure in diabetes.Artificial intelligence is advantageous because it can handle large and complex datasets from a variety of sources. With each additional type of data incorporated into a clinical picture of a patient, the calculation becomes increasingly complex and specific. Artificial intelligence is the foundation of emerging medical technologies; it will power the future of diagnosing diabetes complications.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"246-264"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688687/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142692875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carine M Nassar, Robert Dunlea, Alex Montero, April Tweedt, Michelle F Magee
{"title":"Feasibility and Preliminary Behavioral and Clinical Efficacy of a Diabetes Education Chatbot Pilot Among Adults With Type 2 Diabetes.","authors":"Carine M Nassar, Robert Dunlea, Alex Montero, April Tweedt, Michelle F Magee","doi":"10.1177/19322968231178020","DOIUrl":"10.1177/19322968231178020","url":null,"abstract":"<p><strong>Background: </strong>Diabetes self-management education and support (DSMES) improves diabetes outcomes yet remains consistently underutilized. Chatbot technology offers the potential to increase access to and engagement in DSMES. Evidence supporting the case for chatbot uptake and efficacy in people with diabetes (PWD) is needed.</p><p><strong>Method: </strong>A diabetes education and support chatbot was deployed in a regional health care system. Adults with type 2 diabetes with an A1C of 8.0% to 8.9% and/or having recently completed a 12-week diabetes care management program were enrolled in a pilot program. Weekly chats included three elements: knowledge assessment, limited self-reporting of blood glucose data and medication taking behaviors, and education content (short videos and printable materials). A clinician facing dashboard identified need for escalation via flags based on participant responses. Data were collected to assess satisfaction, engagement, and preliminary glycemic outcomes.</p><p><strong>Results: </strong>Over 16 months, 150 PWD (majority above 50 years of age, female, and African American) were enrolled. The unenrollment rate was 5%. Most escalation flags (N = 128) were for hypoglycemia (41%), hyperglycemia (32%), and medication issues (11%). Overall satisfaction was high for chat content, length, and frequency, and 87% reported increased self-care confidence. Enrollees completing more than one chat had a mean drop in A1C of -1.04%, whereas those completing one chat or less had a mean increase in A1C of +0.09% (<i>P</i> = .008).</p><p><strong>Conclusion: </strong>This diabetes education chatbot pilot demonstrated PWD acceptability, satisfaction, and engagement plus preliminary evidence of self-care confidence and A1C improvement. Further efforts are needed to validate these promising early findings.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"54-62"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9579419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julia K Mader, Brian Huffman, Robert Sharon, Gabriela Bucklar, Julia Roetschke
{"title":"Glucagon-like Peptide-1-Based Therapies Do Not Interfere With Blood Glucose Monitoring Systems.","authors":"Julia K Mader, Brian Huffman, Robert Sharon, Gabriela Bucklar, Julia Roetschke","doi":"10.1177/19322968241293810","DOIUrl":"10.1177/19322968241293810","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"272-273"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142501265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Louis A Gomez, Adedolapo Aishat Toye, R Stanley Hum, Samantha Kleinberg
{"title":"Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.","authors":"Louis A Gomez, Adedolapo Aishat Toye, R Stanley Hum, Samantha Kleinberg","doi":"10.1177/19322968231181138","DOIUrl":"10.1177/19322968231181138","url":null,"abstract":"<p><strong>Background: </strong>Simulated data are a powerful tool for research, enabling benchmarking of blood glucose (BG) forecasting and control algorithms. However, expert created models provide an unrealistic view of real-world performance, as they lack the features that make real data challenging, while black-box approaches such as generative adversarial networks do not enable systematic tests to diagnose model performance.</p><p><strong>Methods: </strong>To address this, we propose a method that learns missingness and error properties of continuous glucose monitor (CGM) data collected from people with type 1 diabetes (OpenAPS, OhioT1DM, RCT, and Racial-Disparity), and then augments simulated BG data with these properties. On the task of BG forecasting, we test how well our method brings performance closer to that of real CGM data compared with current simulation practices for missing data (random dropout) and error (Gaussian noise, CGM error model).</p><p><strong>Results: </strong>Our methods had the smallest performance difference versus real data compared with random dropout and Gaussian noise when individually testing the effects of missing data and error on simulated BG in most cases. When combined, our approach was significantly better than Gaussian noise and random dropout for all data sets except OhioT1DM. Our error model significantly improved results on diverse data sets.</p><p><strong>Conclusions: </strong>We find a significant gap between BG forecasting performance on simulated and real data, and our method can be used to close this gap. This will enable researchers to rigorously test algorithms and provide realistic estimates of real-world performance without overfitting to real data or at the expense of data collection.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"114-122"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9775429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicholas J Christakis, Marcella Gioe, Ricardo Gomez, Dania Felipe, Arlette Soros, Robert McCarter, Stuart Chalew
{"title":"Determination of Glucose-Independent Racial Disparity in HbA1c for Youth With Type 1 Diabetes in the Era of Continuous Glucose Monitoring.","authors":"Nicholas J Christakis, Marcella Gioe, Ricardo Gomez, Dania Felipe, Arlette Soros, Robert McCarter, Stuart Chalew","doi":"10.1177/19322968231199113","DOIUrl":"10.1177/19322968231199113","url":null,"abstract":"<p><strong>Background: </strong>The magnitude and importance of higher HbA1c levels not due to mean blood glucose (MBG) in non-Hispanic black (B) versus non-Hispanic white (W) individuals is controversial. We sought to clarify the relationship of HbA1c with glucose data from continuous glucose monitoring (CGM) in a young biracial population.</p><p><strong>Methods: </strong>Glycemic data of 33 B and 85 W, healthy youth with type 1 diabetes (age 14.7 ± 4.8 years, M/F = 51/67, duration of diabetes 5.4 ± 4.7 years) from a factory-calibrated CGM was compared with HbA1c. Hemoglobin glycation index (HGI) = assayed HbA1c - glucose management index (GMI).</p><p><strong>Results: </strong>B patients had higher unadjusted levels of HbA1c, MBG, MBGSD, GMI, and HGI than W patients. Percent glucose time in range (TIR) and percent sensor use (PSU) were lower for B patients. Average HbA1c in B patients 8.3% was higher than 7.7% for W (P < .0001) after statistical adjustment for MBG, age, gender, insulin delivery method, and accounting for a race by PSU interaction effect. Higher HbA1c persisted in B patients when TIR was substituted for MBG. Predicted MBG was higher in B patients at any level of PSU. The 95th percentile for HGI was 0.47 in W patients, and 52% of B patients had HGI ≥ 0.5. Time below range was similar for both.</p><p><strong>Conclusions: </strong>Young B patients have clinically relevant higher average HbA1c at any given level of MBG or TIR than W patients, which may pose an additional risk for diabetes complications development. HGI ≥ 0.5 may be an easy way to identify high-risk patients.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"72-79"},"PeriodicalIF":4.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688699/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10278161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}